Nvidia Debuts GPU-Based Surveillance Platform For Threat Detection

Big data can only be smart data with the right tools. That's becoming increasingly hard for military and crime investigators that slice, dice and parse terabytes of image and video data to detect threats. On Wednesday, Nvidia announced a solution it says is just what Jack Sparrow might have ordered -- a GPU-accelerated geospatial intelligence platform that allows security analysts to zip through raw data, images and video to detect threats fast and accurately.

The Nvidia platform, called GeoInt Accelerator, consists of the Nvidia Tesla GPU, software applications for geospatial intelligence analysis, and supports custom-advanced application development libraries. Nvidia claims its platform can help companies analyze high-resolution satellite imagery, facial recognition in surveillance video and video collected by drones 10 times faster than systems with CPUs alone.

Nvidia is targeting the fast-growing business-intelligence market that in 2012 was worth $13.8 billion, according to Gartner. The market is projected to be worth $17.1 billion by 2016, Gartner reports. The security intelligence segment of the business intelligence market is growing extremely fast.

"The need is real," said Sean Forbes, CEO of Aptus Technologies, an intelligence analytics firm that processes data for the military and crime investigators. The challenge is keeping pace with the growing data volume, he says. "Most of the intelligence we process today is graphics-based. Nvidia is spot-on for identifying a need," Forbes said.

The new Nvidia GPU-accelerated platform is an update to the company's CUDA parallel computing platform, originally introduced in 2006 and designed for workstations, compute clusters and supercomputers. CUDA technology is based on Nvidia hardware originally developed for graphics cards targeting consumer PCs.

GPUs, as opposed to CPUs, have an advantage in processing image-based satellite or geographic information system (GIS)-driven data, said Roy Kim, group product manager at the Tesla Accelerated Computing Business Unit at Nvidia. GPUs are designed to process images and video, whereas CPUs juggle mostly linear data workloads. Kim said its GPU platform can handle 100 calculations per second compared to one per second with a CPU.

Doron Kempel says selling hyper-convergence can be challenging for solution providers, but success will come from taking business from competitors that are unprepared or hesitant to embrace the technology.